{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Untitled28.ipynb","provenance":[],"collapsed_sections":[],"mount_file_id":"1AAMTVQGrPM5gTdNNHPXFALdbNIpUPmZV","authorship_tag":"ABX9TyNQ0FiOdolJiFjOSqpyy82U"},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"Lj3kRST2IrBF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1606048137244,"user_tz":-120,"elapsed":79788,"user":{"displayName":"Prof Soft","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiaDqV9emD_XHcCKCtBVYe0PPmysd-B7JfNYPmZ=s64","userId":"08982446338325414900"}},"outputId":"47ab16e2-5ee4-4e13-b55b-982788cb3c61"},"source":["!pip install tensorflow==2.1.0"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting tensorflow==2.1.0\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/85/d4/c0cd1057b331bc38b65478302114194bd8e1b9c2bbc06e300935c0e93d90/tensorflow-2.1.0-cp36-cp36m-manylinux2010_x86_64.whl (421.8MB)\n","\u001b[K     |████████████████████████████████| 421.8MB 36kB/s \n","\u001b[?25hRequirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (3.12.4)\n","Collecting gast==0.2.2\n","  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n","Collecting keras-applications>=1.0.8\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50kB)\n","\u001b[K     |████████████████████████████████| 51kB 5.6MB/s \n","\u001b[?25hRequirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (1.15.0)\n","Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (1.33.2)\n","Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (1.1.2)\n","Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (1.1.0)\n","Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (0.10.0)\n","Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (1.18.5)\n","Collecting tensorboard<2.2.0,>=2.1.0\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d9/41/bbf49b61370e4f4d245d4c6051dfb6db80cec672605c91b1652ac8cc3d38/tensorboard-2.1.1-py3-none-any.whl (3.8MB)\n","\u001b[K     |████████████████████████████████| 3.9MB 40.1MB/s \n","\u001b[?25hCollecting tensorflow-estimator<2.2.0,>=2.1.0rc0\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/18/90/b77c328a1304437ab1310b463e533fa7689f4bfc41549593056d812fab8e/tensorflow_estimator-2.1.0-py2.py3-none-any.whl (448kB)\n","\u001b[K     |████████████████████████████████| 450kB 42.0MB/s \n","\u001b[?25hRequirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (0.8.1)\n","Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (0.35.1)\n","Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (1.12.1)\n","Requirement already satisfied: scipy==1.4.1; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (1.4.1)\n","Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.1.0) (0.2.0)\n","Requirement already satisfied: opt-einsum>=2.3.2 in 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in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (4.1.1)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (2020.6.20)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (1.24.3)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (3.0.4)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (3.4.0)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (3.1.0)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<2.2.0,>=2.1.0->tensorflow==2.1.0) (0.4.8)\n","Building wheels for collected packages: gast\n","  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7542 sha256=48dc03b7d6f9a444fc02fbf887065429191b1d80da111ac25b3d2ec6957c8038\n","  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n","Successfully built gast\n","\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n","Installing collected packages: gast, keras-applications, tensorboard, tensorflow-estimator, tensorflow\n","  Found existing installation: gast 0.3.3\n","    Uninstalling gast-0.3.3:\n","      Successfully uninstalled gast-0.3.3\n","  Found existing installation: tensorboard 2.3.0\n","    Uninstalling tensorboard-2.3.0:\n","      Successfully uninstalled tensorboard-2.3.0\n","  Found existing installation: tensorflow-estimator 2.3.0\n","    Uninstalling tensorflow-estimator-2.3.0:\n","      Successfully uninstalled tensorflow-estimator-2.3.0\n","  Found existing installation: tensorflow 2.3.0\n","    Uninstalling tensorflow-2.3.0:\n","      Successfully uninstalled tensorflow-2.3.0\n","Successfully installed gast-0.2.2 keras-applications-1.0.8 tensorboard-2.1.1 tensorflow-2.1.0 tensorflow-estimator-2.1.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"07XKrBKcaOXT"},"source":["import tensorflow as tf\n","from tensorflow import keras\n","import os\n","import cv2\n","import matplotlib.pyplot as plt\n","import numpy as np\n","from tensorflow.keras.utils import to_categorical\n","from numpy import argmax"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"kIHkqekjco6s"},"source":["x=np.zeros((183,96,96,3),dtype=np.float32)\n","ye=np.zeros((183,1),dtype=np.float32)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_TcmtPocbQ-C","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1606048419099,"user_tz":-120,"elapsed":73289,"user":{"displayName":"Prof Soft","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiaDqV9emD_XHcCKCtBVYe0PPmysd-B7JfNYPmZ=s64","userId":"08982446338325414900"}},"outputId":"172a5aa5-e647-40ec-ecea-53406e24a323"},"source":["p=0\n","for filename in os.listdir('/content/drive/MyDrive/players'):\n","    for player in os.listdir('/content/drive/MyDrive/players'+'/'+filename):\n","        img=cv2.imread('/content/drive/MyDrive/players'+'/'+filename+'/'+player)\n","        x[p]=cv2.resize(img,(96,96))\n","        ye[p]=int(filename)\n","        p=p+1\n","y = to_categorical(ye)\n","print(y)   "],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [1. 0. 0.]\n"," [0. 0. 1.]\n"," [0. 0. 1.]\n"," [0. 0. 1.]\n"," [0. 0. 1.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 1. 0.]\n"," [0. 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y)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"BeHdmj3jhlZq"},"source":["inputs=keras.layers.Input((96,96,3))\n","l1=keras.layers.Conv2D(32,kernel_size=(3,3),activation='relu')(inputs)\n","l2=keras.layers.MaxPool2D((2,2))(l1)\n","\n","\n","l3=keras.layers.Conv2D(16,(3,3),activation='relu')(l2)\n","l4=keras.layers.MaxPool2D((2,2))(l3)\n","\n","l5=keras.layers.Conv2D(8,(3,3),activation='relu')(l4)\n","l6=keras.layers.MaxPool2D((2,2))(l5)\n","\n","l7=keras.layers.Conv2D(2,(3,3),activation='relu')(l6)\n","\n","\n","l8=keras.layers.Flatten()(l7)\n","l9=keras.layers.Dense(256,activation='relu')(l8)\n","l10=keras.layers.Dropout(0.5)(l9)\n","l11=keras.layers.Dense(3,activation='sigmoid')(l10)\n","model=keras.Model(inputs=inputs,outputs=l11)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"FKM2OCpUzuEP"},"source":["model.compile(loss='binary_crossentropy', \n","              optimizer='rmsprop', \n","              metrics=['accuracy'])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x1827L730UEv","executionInfo":{"status":"ok","timestamp":1606050048032,"user_tz":-120,"elapsed":260241,"user":{"displayName":"Prof Soft","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiaDqV9emD_XHcCKCtBVYe0PPmysd-B7JfNYPmZ=s64","userId":"08982446338325414900"}},"outputId":"57656dd7-803f-4ecf-99f0-b74071836566"},"source":["model.fit(v,u,6,500)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Train on 183 samples\n","Epoch 1/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3719e-07 - accuracy: 1.0000\n","Epoch 2/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.9729e-08 - accuracy: 1.0000\n","Epoch 3/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0035 - accuracy: 0.9982\n","Epoch 4/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6248e-06 - accuracy: 1.0000\n","Epoch 5/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 2.5960e-08 - accuracy: 1.0000\n","Epoch 6/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6441e-08 - accuracy: 1.0000\n","Epoch 7/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.1259e-09 - accuracy: 1.0000\n","Epoch 8/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1516e-08 - accuracy: 1.0000\n","Epoch 9/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 6.3298e-09 - accuracy: 1.0000\n","Epoch 10/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 0.0279 - accuracy: 0.9964\n","Epoch 11/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.5407e-06 - accuracy: 1.0000\n","Epoch 12/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.5374e-07 - accuracy: 1.0000\n","Epoch 13/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 1.7656e-06 - accuracy: 1.0000\n","Epoch 14/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 4.8482e-08 - accuracy: 1.0000\n","Epoch 15/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 1.0672e-08 - accuracy: 1.0000\n","Epoch 16/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.3945e-08 - accuracy: 1.0000\n","Epoch 17/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.9320e-08 - accuracy: 1.0000\n","Epoch 18/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 5.6042e-05 - accuracy: 1.0000\n","Epoch 19/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.0938e-08 - accuracy: 1.0000\n","Epoch 20/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 1.2823e-04 - accuracy: 1.0000\n","Epoch 21/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 2.6019e-09 - accuracy: 1.0000\n","Epoch 22/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0056 - accuracy: 0.9964\n","Epoch 23/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.3439e-07 - accuracy: 1.0000\n","Epoch 24/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.5782e-07 - accuracy: 1.0000\n","Epoch 25/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.9283e-08 - accuracy: 1.0000\n","Epoch 26/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2720e-06 - accuracy: 1.0000\n","Epoch 27/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.4753e-09 - accuracy: 1.0000\n","Epoch 28/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 2.8365e-09 - accuracy: 1.0000\n","Epoch 29/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.5999e-05 - accuracy: 1.0000\n","Epoch 30/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.3709e-06 - accuracy: 1.0000\n","Epoch 31/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0124 - accuracy: 0.9982\n","Epoch 32/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.6559e-07 - accuracy: 1.0000\n","Epoch 33/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.0053e-07 - accuracy: 1.0000\n","Epoch 34/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 4.2269e-06 - accuracy: 1.0000\n","Epoch 35/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 1.8137e-08 - accuracy: 1.0000\n","Epoch 36/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.8948e-08 - accuracy: 1.0000\n","Epoch 37/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.7359e-08 - accuracy: 1.0000\n","Epoch 38/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.6417e-09 - accuracy: 1.0000\n","Epoch 39/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3256e-07 - accuracy: 1.0000\n","Epoch 40/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2015e-09 - accuracy: 1.0000\n","Epoch 41/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.5929e-09 - accuracy: 1.0000\n","Epoch 42/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 5.0058e-04 - accuracy: 1.0000\n","Epoch 43/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.5872e-08 - accuracy: 1.0000\n","Epoch 44/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0011 - accuracy: 1.0000\n","Epoch 45/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.5323e-08 - accuracy: 1.0000\n","Epoch 46/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.0715e-05 - accuracy: 1.0000\n","Epoch 47/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.2593e-10 - accuracy: 1.0000\n","Epoch 48/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.3812e-08 - accuracy: 1.0000\n","Epoch 49/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.1744e-07 - accuracy: 1.0000\n","Epoch 50/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.8102e-08 - accuracy: 1.0000\n","Epoch 51/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 6.8164e-10 - accuracy: 1.0000\n","Epoch 52/500\n","183/183 [==============================] - 2s 10ms/sample - loss: 1.9232e-08 - accuracy: 1.0000\n","Epoch 53/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0022 - accuracy: 0.9982\n","Epoch 54/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.9162e-05 - accuracy: 1.0000\n","Epoch 55/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.3020e-10 - accuracy: 1.0000\n","Epoch 56/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.6023e-07 - accuracy: 1.0000\n","Epoch 57/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0498 - accuracy: 0.9909\n","Epoch 58/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.4953e-05 - accuracy: 1.0000\n","Epoch 59/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.2024e-07 - accuracy: 1.0000\n","Epoch 60/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.4031e-08 - accuracy: 1.0000\n","Epoch 61/500\n","183/183 [==============================] - 2s 13ms/sample - loss: 8.0715e-06 - accuracy: 1.0000\n","Epoch 62/500\n","183/183 [==============================] - 3s 15ms/sample - loss: 5.1381e-07 - accuracy: 1.0000\n","Epoch 63/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.6275e-11 - accuracy: 1.0000\n","Epoch 64/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.0170e-09 - accuracy: 1.0000\n","Epoch 65/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.7188e-09 - accuracy: 1.0000\n","Epoch 66/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0042 - accuracy: 0.9982\n","Epoch 67/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0055 - accuracy: 0.9982\n","Epoch 68/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.4239e-05 - accuracy: 1.0000\n","Epoch 69/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.9091e-09 - accuracy: 1.0000\n","Epoch 70/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.7148e-09 - accuracy: 1.0000\n","Epoch 71/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.1059e-09 - accuracy: 1.0000\n","Epoch 72/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.3208e-08 - accuracy: 1.0000\n","Epoch 73/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.9850e-09 - accuracy: 1.0000\n","Epoch 74/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2030e-10 - accuracy: 1.0000\n","Epoch 75/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.2771e-09 - accuracy: 1.0000\n","Epoch 76/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.4847e-10 - accuracy: 1.0000\n","Epoch 77/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.4126e-09 - accuracy: 1.0000\n","Epoch 78/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7159e-09 - accuracy: 1.0000\n","Epoch 79/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.9909e-11 - accuracy: 1.0000\n","Epoch 80/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.3629e-11 - accuracy: 1.0000\n","Epoch 81/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.6191e-10 - accuracy: 1.0000\n","Epoch 82/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.3743e-14 - accuracy: 1.0000\n","Epoch 83/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0038 - accuracy: 0.9982\n","Epoch 84/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.1821e-08 - accuracy: 1.0000\n","Epoch 85/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2504e-06 - accuracy: 1.0000\n","Epoch 86/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.2403e-07 - accuracy: 1.0000\n","Epoch 87/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2309e-07 - accuracy: 1.0000\n","Epoch 88/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.8048e-09 - accuracy: 1.0000\n","Epoch 89/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.4675e-07 - accuracy: 1.0000\n","Epoch 90/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.0403e-06 - accuracy: 1.0000\n","Epoch 91/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.3527e-04 - accuracy: 1.0000\n","Epoch 92/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.6081e-07 - accuracy: 1.0000\n","Epoch 93/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1085e-05 - accuracy: 1.0000\n","Epoch 94/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0409 - accuracy: 0.9964\n","Epoch 95/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.2412e-06 - accuracy: 1.0000\n","Epoch 96/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.4737e-07 - accuracy: 1.0000\n","Epoch 97/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.1298e-07 - accuracy: 1.0000\n","Epoch 98/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0336e-06 - accuracy: 1.0000\n","Epoch 99/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.7726e-08 - accuracy: 1.0000\n","Epoch 100/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.6142e-05 - accuracy: 1.0000\n","Epoch 101/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.1588e-07 - accuracy: 1.0000\n","Epoch 102/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.8692e-07 - accuracy: 1.0000\n","Epoch 103/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.8034e-04 - accuracy: 1.0000\n","Epoch 104/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.9031e-04 - accuracy: 1.0000\n","Epoch 105/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.6457e-07 - accuracy: 1.0000\n","Epoch 106/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.4040e-06 - accuracy: 1.0000\n","Epoch 107/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.5450e-08 - accuracy: 1.0000\n","Epoch 108/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.4893e-08 - accuracy: 1.0000\n","Epoch 109/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2194e-10 - accuracy: 1.0000\n","Epoch 110/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.9729e-11 - accuracy: 1.0000\n","Epoch 111/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.9661e-09 - accuracy: 1.0000\n","Epoch 112/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.6927e-12 - accuracy: 1.0000\n","Epoch 113/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.9912e-11 - accuracy: 1.0000\n","Epoch 114/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.7758e-11 - accuracy: 1.0000\n","Epoch 115/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.2695e-11 - accuracy: 1.0000\n","Epoch 116/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.7665e-11 - accuracy: 1.0000\n","Epoch 117/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.8405e-10 - accuracy: 1.0000\n","Epoch 118/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0620e-11 - accuracy: 1.0000\n","Epoch 119/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0688e-11 - accuracy: 1.0000\n","Epoch 120/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.9512e-10 - accuracy: 1.0000\n","Epoch 121/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.7955e-12 - accuracy: 1.0000\n","Epoch 122/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.0811e-12 - accuracy: 1.0000\n","Epoch 123/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.8585e-11 - accuracy: 1.0000\n","Epoch 124/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1807e-09 - accuracy: 1.0000\n","Epoch 125/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.2783e-04 - accuracy: 1.0000\n","Epoch 126/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0018 - accuracy: 0.9982\n","Epoch 127/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.5085e-04 - accuracy: 1.0000\n","Epoch 128/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.9400e-10 - accuracy: 1.0000\n","Epoch 129/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.2457e-06 - accuracy: 1.0000\n","Epoch 130/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.1733e-08 - accuracy: 1.0000\n","Epoch 131/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.7718e-10 - accuracy: 1.0000\n","Epoch 132/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.3647e-09 - accuracy: 1.0000\n","Epoch 133/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.0490e-08 - accuracy: 1.0000\n","Epoch 134/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.9490e-08 - accuracy: 1.0000\n","Epoch 135/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0036 - accuracy: 0.9982\n","Epoch 136/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3202e-04 - accuracy: 1.0000\n","Epoch 137/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.9003e-07 - accuracy: 1.0000\n","Epoch 138/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.4832e-08 - accuracy: 1.0000\n","Epoch 139/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7237e-07 - accuracy: 1.0000\n","Epoch 140/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0372e-08 - accuracy: 1.0000\n","Epoch 141/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.7370e-11 - accuracy: 1.0000\n","Epoch 142/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0760 - accuracy: 0.9927\n","Epoch 143/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1577e-04 - accuracy: 1.0000\n","Epoch 144/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.2500e-10 - accuracy: 1.0000\n","Epoch 145/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.8853e-05 - accuracy: 1.0000\n","Epoch 146/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.4394e-08 - accuracy: 1.0000\n","Epoch 147/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3781e-08 - accuracy: 1.0000\n","Epoch 148/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.3360e-09 - accuracy: 1.0000\n","Epoch 149/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.8522e-08 - accuracy: 1.0000\n","Epoch 150/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.0313e-08 - accuracy: 1.0000\n","Epoch 151/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1300e-09 - accuracy: 1.0000\n","Epoch 152/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.8977e-13 - accuracy: 1.0000\n","Epoch 153/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.7487e-05 - accuracy: 1.0000\n","Epoch 154/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.3897e-08 - accuracy: 1.0000\n","Epoch 155/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.8359e-08 - accuracy: 1.0000\n","Epoch 156/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1860e-07 - accuracy: 1.0000\n","Epoch 157/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.9780e-12 - accuracy: 1.0000\n","Epoch 158/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.9084e-11 - accuracy: 1.0000\n","Epoch 159/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2128e-10 - accuracy: 1.0000\n","Epoch 160/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.8032e-08 - accuracy: 1.0000\n","Epoch 161/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0488 - accuracy: 0.9964\n","Epoch 162/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1495e-07 - accuracy: 1.0000\n","Epoch 163/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.2045e-11 - accuracy: 1.0000\n","Epoch 164/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6146e-09 - accuracy: 1.0000\n","Epoch 165/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.9140e-09 - accuracy: 1.0000\n","Epoch 166/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6404e-10 - accuracy: 1.0000\n","Epoch 167/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.5104e-07 - accuracy: 1.0000\n","Epoch 168/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.1503e-09 - accuracy: 1.0000\n","Epoch 169/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.8262e-09 - accuracy: 1.0000\n","Epoch 170/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1525e-11 - accuracy: 1.0000\n","Epoch 171/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0530e-09 - accuracy: 1.0000\n","Epoch 172/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.5090e-13 - accuracy: 1.0000\n","Epoch 173/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.0036e-12 - accuracy: 1.0000\n","Epoch 174/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.0855e-10 - accuracy: 1.0000\n","Epoch 175/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.7610e-09 - accuracy: 1.0000\n","Epoch 176/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.0248e-11 - accuracy: 1.0000\n","Epoch 177/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.1531e-12 - accuracy: 1.0000\n","Epoch 178/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3191e-15 - accuracy: 1.0000\n","Epoch 179/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0799e-10 - accuracy: 1.0000\n","Epoch 180/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.2735e-12 - accuracy: 1.0000\n","Epoch 181/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.5191e-13 - accuracy: 1.0000\n","Epoch 182/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.5769e-13 - accuracy: 1.0000\n","Epoch 183/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.4422e-10 - accuracy: 1.0000\n","Epoch 184/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7570e-13 - accuracy: 1.0000\n","Epoch 185/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.7765e-07 - accuracy: 1.0000\n","Epoch 186/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.2906e-14 - accuracy: 1.0000\n","Epoch 187/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.4096e-14 - accuracy: 1.0000\n","Epoch 188/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.2515e-11 - accuracy: 1.0000\n","Epoch 189/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.2841e-12 - accuracy: 1.0000\n","Epoch 190/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.2777e-14 - accuracy: 1.0000\n","Epoch 191/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.0941e-11 - accuracy: 1.0000\n","Epoch 192/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.4284e-13 - accuracy: 1.0000\n","Epoch 193/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.5455e-10 - accuracy: 1.0000\n","Epoch 194/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.0653e-09 - accuracy: 1.0000\n","Epoch 195/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0095 - accuracy: 0.9982\n","Epoch 196/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.4375e-09 - accuracy: 1.0000\n","Epoch 197/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.3314e-08 - accuracy: 1.0000\n","Epoch 198/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.6504e-12 - accuracy: 1.0000\n","Epoch 199/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.6057e-08 - accuracy: 1.0000\n","Epoch 200/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1464e-08 - accuracy: 1.0000\n","Epoch 201/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.6804e-09 - accuracy: 1.0000\n","Epoch 202/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.1418e-10 - accuracy: 1.0000\n","Epoch 203/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1835e-10 - accuracy: 1.0000\n","Epoch 204/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.3014e-09 - accuracy: 1.0000\n","Epoch 205/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.0981e-10 - accuracy: 1.0000\n","Epoch 206/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.1673e-12 - accuracy: 1.0000\n","Epoch 207/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0744 - accuracy: 0.9945\n","Epoch 208/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 5.2268e-11 - accuracy: 1.0000\n","Epoch 209/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 3.1464e-07 - accuracy: 1.0000\n","Epoch 210/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.1220e-11 - accuracy: 1.0000\n","Epoch 211/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.4197e-10 - accuracy: 1.0000\n","Epoch 212/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1965e-13 - accuracy: 1.0000\n","Epoch 213/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3158e-10 - accuracy: 1.0000\n","Epoch 214/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.4574e-15 - accuracy: 1.0000\n","Epoch 215/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.2147e-07 - accuracy: 1.0000\n","Epoch 216/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.1141e-10 - accuracy: 1.0000\n","Epoch 217/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7270e-13 - accuracy: 1.0000\n","Epoch 218/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.8153e-13 - accuracy: 1.0000\n","Epoch 219/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6401e-10 - accuracy: 1.0000\n","Epoch 220/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.4305e-13 - accuracy: 1.0000\n","Epoch 221/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.9323e-14 - accuracy: 1.0000\n","Epoch 222/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.8275e-14 - accuracy: 1.0000\n","Epoch 223/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2111e-11 - accuracy: 1.0000\n","Epoch 224/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.4124e-08 - accuracy: 1.0000\n","Epoch 225/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.5260e-14 - accuracy: 1.0000\n","Epoch 226/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.9059e-12 - accuracy: 1.0000\n","Epoch 227/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.2460e-14 - accuracy: 1.0000\n","Epoch 228/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.4310e-12 - accuracy: 1.0000\n","Epoch 229/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.3211e-13 - accuracy: 1.0000\n","Epoch 230/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.9801e-13 - accuracy: 1.0000\n","Epoch 231/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.7582e-13 - accuracy: 1.0000\n","Epoch 232/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.0514e-13 - accuracy: 1.0000\n","Epoch 233/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3238e-14 - accuracy: 1.0000\n","Epoch 234/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.6951e-15 - accuracy: 1.0000\n","Epoch 235/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.2421e-10 - accuracy: 1.0000\n","Epoch 236/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.0329e-15 - accuracy: 1.0000\n","Epoch 237/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0815e-12 - accuracy: 1.0000\n","Epoch 238/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.5525e-14 - accuracy: 1.0000\n","Epoch 239/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.7559e-11 - accuracy: 1.0000\n","Epoch 240/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.0115e-16 - accuracy: 1.0000\n","Epoch 241/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0073 - accuracy: 0.9964\n","Epoch 242/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7433e-07 - accuracy: 1.0000\n","Epoch 243/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 0.0030 - accuracy: 0.9982\n","Epoch 244/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.3514e-04 - accuracy: 1.0000\n","Epoch 245/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.4580e-08 - accuracy: 1.0000\n","Epoch 246/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.4369e-10 - accuracy: 1.0000\n","Epoch 247/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.4886e-06 - accuracy: 1.0000\n","Epoch 248/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.7025e-07 - accuracy: 1.0000\n","Epoch 249/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.3086e-07 - accuracy: 1.0000\n","Epoch 250/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.3304e-06 - accuracy: 1.0000\n","Epoch 251/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.6809e-08 - accuracy: 1.0000\n","Epoch 252/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.5888e-06 - accuracy: 1.0000\n","Epoch 253/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1465e-15 - accuracy: 1.0000\n","Epoch 254/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.4263e-16 - accuracy: 1.0000\n","Epoch 255/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.1390e-15 - accuracy: 1.0000\n","Epoch 256/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.4041e-11 - accuracy: 1.0000\n","Epoch 257/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.8179e-07 - accuracy: 1.0000\n","Epoch 258/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.2629e-14 - accuracy: 1.0000\n","Epoch 259/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.9375e-12 - accuracy: 1.0000\n","Epoch 260/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.1246e-07 - accuracy: 1.0000\n","Epoch 261/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.5261e-08 - accuracy: 1.0000\n","Epoch 262/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7416e-09 - accuracy: 1.0000\n","Epoch 263/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1062e-12 - accuracy: 1.0000\n","Epoch 264/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.2165e-12 - accuracy: 1.0000\n","Epoch 265/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.6025e-14 - accuracy: 1.0000\n","Epoch 266/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.7556e-09 - accuracy: 1.0000\n","Epoch 267/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0087e-10 - accuracy: 1.0000\n","Epoch 268/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.0561e-09 - accuracy: 1.0000\n","Epoch 269/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3561e-11 - accuracy: 1.0000\n","Epoch 270/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.7387e-14 - accuracy: 1.0000\n","Epoch 271/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.7126e-15 - accuracy: 1.0000\n","Epoch 272/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.2913e-15 - accuracy: 1.0000\n","Epoch 273/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.2250e-11 - accuracy: 1.0000\n","Epoch 274/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0527e-12 - accuracy: 1.0000\n","Epoch 275/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 5.4518e-14 - accuracy: 1.0000\n","Epoch 276/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 3.8720e-13 - accuracy: 1.0000\n","Epoch 277/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.4047e-14 - accuracy: 1.0000\n","Epoch 278/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.2564e-14 - accuracy: 1.0000\n","Epoch 279/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.4385e-06 - accuracy: 1.0000\n","Epoch 280/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.2361e-10 - accuracy: 1.0000\n","Epoch 281/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2013e-10 - accuracy: 1.0000\n","Epoch 282/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.8853e-11 - accuracy: 1.0000\n","Epoch 283/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.5591e-09 - accuracy: 1.0000\n","Epoch 284/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.6505e-11 - accuracy: 1.0000\n","Epoch 285/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.9977e-14 - accuracy: 1.0000\n","Epoch 286/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6013e-17 - accuracy: 1.0000\n","Epoch 287/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.2978e-12 - accuracy: 1.0000\n","Epoch 288/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.6762e-11 - accuracy: 1.0000\n","Epoch 289/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.1460e-13 - accuracy: 1.0000\n","Epoch 290/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1379e-15 - accuracy: 1.0000\n","Epoch 291/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1133e-16 - accuracy: 1.0000\n","Epoch 292/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.6078e-13 - accuracy: 1.0000\n","Epoch 293/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2177e-13 - accuracy: 1.0000\n","Epoch 294/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.3675e-14 - accuracy: 1.0000\n","Epoch 295/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.9985e-12 - accuracy: 1.0000\n","Epoch 296/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3104e-14 - accuracy: 1.0000\n","Epoch 297/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.6756e-16 - accuracy: 1.0000\n","Epoch 298/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6569e-17 - accuracy: 1.0000\n","Epoch 299/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.8753e-14 - accuracy: 1.0000\n","Epoch 300/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.5252e-12 - accuracy: 1.0000\n","Epoch 301/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.3458e-14 - accuracy: 1.0000\n","Epoch 302/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.8835e-12 - accuracy: 1.0000\n","Epoch 303/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2407e-13 - accuracy: 1.0000\n","Epoch 304/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.5879e-11 - accuracy: 1.0000\n","Epoch 305/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1764e-04 - accuracy: 1.0000\n","Epoch 306/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.3060e-07 - accuracy: 1.0000\n","Epoch 307/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.6776e-12 - accuracy: 1.0000\n","Epoch 308/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.1273e-10 - accuracy: 1.0000\n","Epoch 309/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.2588e-08 - accuracy: 1.0000\n","Epoch 310/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.4247e-10 - accuracy: 1.0000\n","Epoch 311/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.6146e-16 - accuracy: 1.0000\n","Epoch 312/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.2221e-14 - accuracy: 1.0000\n","Epoch 313/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.5543e-10 - accuracy: 1.0000\n","Epoch 314/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.9376e-12 - accuracy: 1.0000\n","Epoch 315/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1891e-14 - accuracy: 1.0000\n","Epoch 316/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.7282e-13 - accuracy: 1.0000\n","Epoch 317/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.7802e-08 - accuracy: 1.0000\n","Epoch 318/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.5056e-08 - accuracy: 1.0000\n","Epoch 319/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.9075e-17 - accuracy: 1.0000\n","Epoch 320/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1045e-16 - accuracy: 1.0000\n","Epoch 321/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.4182e-13 - accuracy: 1.0000\n","Epoch 322/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0649e-13 - accuracy: 1.0000\n","Epoch 323/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.6848e-12 - accuracy: 1.0000\n","Epoch 324/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.7412e-16 - accuracy: 1.0000\n","Epoch 325/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.1302e-17 - accuracy: 1.0000\n","Epoch 326/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.9194e-16 - accuracy: 1.0000\n","Epoch 327/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.1359e-08 - accuracy: 1.0000\n","Epoch 328/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.6734e-13 - accuracy: 1.0000\n","Epoch 329/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.0471e-11 - accuracy: 1.0000\n","Epoch 330/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.6721e-08 - accuracy: 1.0000\n","Epoch 331/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.5226e-12 - accuracy: 1.0000\n","Epoch 332/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.1795e-09 - accuracy: 1.0000\n","Epoch 333/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.7609e-13 - accuracy: 1.0000\n","Epoch 334/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.7469e-12 - accuracy: 1.0000\n","Epoch 335/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6920e-12 - accuracy: 1.0000\n","Epoch 336/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.2295e-12 - accuracy: 1.0000\n","Epoch 337/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.0667e-12 - accuracy: 1.0000\n","Epoch 338/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.4968e-12 - accuracy: 1.0000\n","Epoch 339/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0883e-13 - accuracy: 1.0000\n","Epoch 340/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.2600e-08 - accuracy: 1.0000\n","Epoch 341/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.8084e-14 - accuracy: 1.0000\n","Epoch 342/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.3314e-16 - accuracy: 1.0000\n","Epoch 343/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6558e-12 - accuracy: 1.0000\n","Epoch 344/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0660e-14 - accuracy: 1.0000\n","Epoch 345/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.0349e-14 - accuracy: 1.0000\n","Epoch 346/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0030e-09 - accuracy: 1.0000\n","Epoch 347/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.9898e-14 - accuracy: 1.0000\n","Epoch 348/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.1684e-14 - accuracy: 1.0000\n","Epoch 349/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 1.2624e-12 - accuracy: 1.0000\n","Epoch 350/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7542e-13 - accuracy: 1.0000\n","Epoch 351/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.2118e-13 - accuracy: 1.0000\n","Epoch 352/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2261e-16 - accuracy: 1.0000\n","Epoch 353/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.3599e-11 - accuracy: 1.0000\n","Epoch 354/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6406e-15 - accuracy: 1.0000\n","Epoch 355/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.6791e-12 - accuracy: 1.0000\n","Epoch 356/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.5202e-15 - accuracy: 1.0000\n","Epoch 357/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.9286e-13 - accuracy: 1.0000\n","Epoch 358/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 7.8340e-13 - accuracy: 1.0000\n","Epoch 359/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.4149e-13 - accuracy: 1.0000\n","Epoch 360/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.6676e-13 - accuracy: 1.0000\n","Epoch 361/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 4.7796e-13 - accuracy: 1.0000\n","Epoch 362/500\n","183/183 [==============================] - 3s 17ms/sample - loss: 1.0188e-10 - accuracy: 1.0000\n","Epoch 363/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.2539e-15 - accuracy: 1.0000\n","Epoch 364/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 5.3279e-12 - accuracy: 1.0000\n","Epoch 365/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 3.0957e-10 - accuracy: 1.0000\n","Epoch 366/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.2886e-15 - accuracy: 1.0000\n","Epoch 367/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.1785e-15 - accuracy: 1.0000\n","Epoch 368/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0695e-14 - accuracy: 1.0000\n","Epoch 369/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.7376e-16 - accuracy: 1.0000\n","Epoch 370/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7953e-14 - accuracy: 1.0000\n","Epoch 371/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.7178e-14 - accuracy: 1.0000\n","Epoch 372/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6083e-15 - accuracy: 1.0000\n","Epoch 373/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1883e-14 - accuracy: 1.0000\n","Epoch 374/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.9797e-14 - accuracy: 1.0000\n","Epoch 375/500\n","183/183 [==============================] - 4s 21ms/sample - loss: 2.3766e-16 - accuracy: 1.0000\n","Epoch 376/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2240e-12 - accuracy: 1.0000\n","Epoch 377/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 1.3126e-14 - accuracy: 1.0000\n","Epoch 378/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.9516e-14 - accuracy: 1.0000\n","Epoch 379/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.5460e-16 - accuracy: 1.0000\n","Epoch 380/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.5090e-15 - accuracy: 1.0000\n","Epoch 381/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1158e-14 - accuracy: 1.0000\n","Epoch 382/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3223e-15 - accuracy: 1.0000\n","Epoch 383/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.2447e-12 - accuracy: 1.0000\n","Epoch 384/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6984e-17 - accuracy: 1.0000\n","Epoch 385/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.9099e-14 - accuracy: 1.0000\n","Epoch 386/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.1928e-12 - accuracy: 1.0000\n","Epoch 387/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.5277e-10 - accuracy: 1.0000\n","Epoch 388/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.5414e-12 - accuracy: 1.0000\n","Epoch 389/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.9807e-14 - accuracy: 1.0000\n","Epoch 390/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.1516e-13 - accuracy: 1.0000\n","Epoch 391/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.5198e-14 - accuracy: 1.0000\n","Epoch 392/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.3353e-15 - accuracy: 1.0000\n","Epoch 393/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3725e-13 - accuracy: 1.0000\n","Epoch 394/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.9898e-15 - accuracy: 1.0000\n","Epoch 395/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.4202e-16 - accuracy: 1.0000\n","Epoch 396/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.9682e-10 - accuracy: 1.0000\n","Epoch 397/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6902e-13 - accuracy: 1.0000\n","Epoch 398/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.3240e-15 - accuracy: 1.0000\n","Epoch 399/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.9703e-11 - accuracy: 1.0000\n","Epoch 400/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.7299e-16 - accuracy: 1.0000\n","Epoch 401/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.7419e-14 - accuracy: 1.0000\n","Epoch 402/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.3274e-12 - accuracy: 1.0000\n","Epoch 403/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.3768e-13 - accuracy: 1.0000\n","Epoch 404/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.9019e-14 - accuracy: 1.0000\n","Epoch 405/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2827e-15 - accuracy: 1.0000\n","Epoch 406/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.4992e-13 - accuracy: 1.0000\n","Epoch 407/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.2080e-14 - accuracy: 1.0000\n","Epoch 408/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1811e-13 - accuracy: 1.0000\n","Epoch 409/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.2910e-12 - accuracy: 1.0000\n","Epoch 410/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.2545e-17 - accuracy: 1.0000\n","Epoch 411/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.5645e-11 - accuracy: 1.0000\n","Epoch 412/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.2513e-15 - accuracy: 1.0000\n","Epoch 413/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.9936e-12 - accuracy: 1.0000\n","Epoch 414/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.7897e-14 - accuracy: 1.0000\n","Epoch 415/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.8877e-15 - accuracy: 1.0000\n","Epoch 416/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7788e-13 - accuracy: 1.0000\n","Epoch 417/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.0460e-12 - accuracy: 1.0000\n","Epoch 418/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.2438e-16 - accuracy: 1.0000\n","Epoch 419/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.9594e-14 - accuracy: 1.0000\n","Epoch 420/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.3171e-13 - accuracy: 1.0000\n","Epoch 421/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.3267e-12 - accuracy: 1.0000\n","Epoch 422/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.3436e-17 - accuracy: 1.0000\n","Epoch 423/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0079e-15 - accuracy: 1.0000\n","Epoch 424/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6690e-13 - accuracy: 1.0000\n","Epoch 425/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 1.0631e-11 - accuracy: 1.0000\n","Epoch 426/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 3.2044e-16 - accuracy: 1.0000\n","Epoch 427/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 1.9137e-16 - accuracy: 1.0000\n","Epoch 428/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 1.6191e-14 - accuracy: 1.0000\n","Epoch 429/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.9475e-16 - accuracy: 1.0000\n","Epoch 430/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.8891e-12 - accuracy: 1.0000\n","Epoch 431/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3601e-14 - accuracy: 1.0000\n","Epoch 432/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.5894e-15 - accuracy: 1.0000\n","Epoch 433/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.2259e-10 - accuracy: 1.0000\n","Epoch 434/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1840e-13 - accuracy: 1.0000\n","Epoch 435/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.8980e-12 - accuracy: 1.0000\n","Epoch 436/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7662e-14 - accuracy: 1.0000\n","Epoch 437/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3253e-14 - accuracy: 1.0000\n","Epoch 438/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.0922e-12 - accuracy: 1.0000\n","Epoch 439/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.2963e-16 - accuracy: 1.0000\n","Epoch 440/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7930e-10 - accuracy: 1.0000\n","Epoch 441/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.2620e-16 - accuracy: 1.0000\n","Epoch 442/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.8149e-16 - accuracy: 1.0000\n","Epoch 443/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.1347e-10 - accuracy: 1.0000\n","Epoch 444/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3572e-11 - accuracy: 1.0000\n","Epoch 445/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.7083e-17 - accuracy: 1.0000\n","Epoch 446/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.6131e-13 - accuracy: 1.0000\n","Epoch 447/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.6712e-13 - accuracy: 1.0000\n","Epoch 448/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.9963e-13 - accuracy: 1.0000\n","Epoch 449/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.1643e-17 - accuracy: 1.0000\n","Epoch 450/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.6305e-15 - accuracy: 1.0000\n","Epoch 451/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.7632e-11 - accuracy: 1.0000\n","Epoch 452/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.6181e-15 - accuracy: 1.0000\n","Epoch 453/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.9007e-16 - accuracy: 1.0000\n","Epoch 454/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.6374e-14 - accuracy: 1.0000\n","Epoch 455/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.9515e-15 - accuracy: 1.0000\n","Epoch 456/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.6164e-11 - accuracy: 1.0000\n","Epoch 457/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.0610e-12 - accuracy: 1.0000\n","Epoch 458/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.9899e-11 - accuracy: 1.0000\n","Epoch 459/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.4893e-14 - accuracy: 1.0000\n","Epoch 460/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.4169e-15 - accuracy: 1.0000\n","Epoch 461/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.0415e-14 - accuracy: 1.0000\n","Epoch 462/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.8978e-15 - accuracy: 1.0000\n","Epoch 463/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 9.7351e-15 - accuracy: 1.0000\n","Epoch 464/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.6220e-14 - accuracy: 1.0000\n","Epoch 465/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.3611e-12 - accuracy: 1.0000\n","Epoch 466/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 3.6355e-11 - accuracy: 1.0000\n","Epoch 467/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.2472e-12 - accuracy: 1.0000\n","Epoch 468/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.4320e-09 - accuracy: 1.0000\n","Epoch 469/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 7.6965e-10 - accuracy: 1.0000\n","Epoch 470/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.8560e-15 - accuracy: 1.0000\n","Epoch 471/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.7050e-14 - accuracy: 1.0000\n","Epoch 472/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.8643e-17 - accuracy: 1.0000\n","Epoch 473/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.4943e-17 - accuracy: 1.0000\n","Epoch 474/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.7472e-16 - accuracy: 1.0000\n","Epoch 475/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.6093e-13 - accuracy: 1.0000\n","Epoch 476/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.5184e-13 - accuracy: 1.0000\n","Epoch 477/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.9880e-13 - accuracy: 1.0000\n","Epoch 478/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 0.0227 - accuracy: 0.9964\n","Epoch 479/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 2.6235e-14 - accuracy: 1.0000\n","Epoch 480/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 6.9886e-14 - accuracy: 1.0000\n","Epoch 481/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.4046e-14 - accuracy: 1.0000\n","Epoch 482/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.6543e-14 - accuracy: 1.0000\n","Epoch 483/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1577e-13 - accuracy: 1.0000\n","Epoch 484/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 4.1517e-16 - accuracy: 1.0000\n","Epoch 485/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.1230e-15 - accuracy: 1.0000\n","Epoch 486/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7449e-11 - accuracy: 1.0000\n","Epoch 487/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.4404e-15 - accuracy: 1.0000\n","Epoch 488/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.7867e-14 - accuracy: 1.0000\n","Epoch 489/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.1520e-15 - accuracy: 1.0000\n","Epoch 490/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.1378e-18 - accuracy: 1.0000\n","Epoch 491/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 3.0642e-14 - accuracy: 1.0000\n","Epoch 492/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 4.4368e-13 - accuracy: 1.0000\n","Epoch 493/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 5.3786e-12 - accuracy: 1.0000\n","Epoch 494/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 7.5086e-14 - accuracy: 1.0000\n","Epoch 495/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 1.0815e-10 - accuracy: 1.0000\n","Epoch 496/500\n","183/183 [==============================] - 2s 12ms/sample - loss: 4.2977e-14 - accuracy: 1.0000\n","Epoch 497/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.3152e-13 - accuracy: 1.0000\n","Epoch 498/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 6.3722e-16 - accuracy: 1.0000\n","Epoch 499/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 8.9623e-17 - accuracy: 1.0000\n","Epoch 500/500\n","183/183 [==============================] - 2s 11ms/sample - loss: 1.7403e-14 - accuracy: 1.0000\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<tensorflow.python.keras.callbacks.History at 0x7efe3c0f7b00>"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":303},"id":"mxEbF7rP2uqT","executionInfo":{"status":"ok","timestamp":1606050061246,"user_tz":-120,"elapsed":870,"user":{"displayName":"Prof Soft","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiaDqV9emD_XHcCKCtBVYe0PPmysd-B7JfNYPmZ=s64","userId":"08982446338325414900"}},"outputId":"11592434-2a45-4771-860f-6e128028eb83"},"source":["o=110\n","yu=model.predict(np.reshape(v[o],(1,96,96,3)))\n","plt.imshow(v[o])\n","print(argmax(yu))\n","print(argmax(u[o]))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["1\n","1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"0WMVKF_b21hD"},"source":["model.save('model2.h5')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"x1B0N-X69U0f"},"source":[""],"execution_count":null,"outputs":[]}]}